Image Clustering Using Color and Texture

With the advancement in image capturing device, the image data been generated at high volume. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Due to semantic gap between low-level image features and the richness of human semantics, a challenge with image contents is to extract meaning from the data they contain. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Proposed framework focuses on color and texture as feature. Color moment and Gabor filter is used to extract features for image dataset. K-means and hierarchical clustering algorithm is applied to group the image dataset into various clusters.